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This paper presents GAIA, a geometry-aware learning framework for UWB denoising and work-zone reconstruction that couples temporal range modeling with latent anchor-layout estimation. Evaluated on real-world outdoor data, GAIA reduces range MSE by 18.4% and improves polygon IoU by 15.5% over baselines, demonstrating effective boundary-level reconstruction under NLOS conditions.